Physics-Based Manipulation Planning in Cluttered Human Environments

This thesis presents a series of planners and algorithms for manipulation in cluttered human environments. The focus is on using physics-based predictions, particularly for pushing operations, as an effective way to address the manipulation challenges posed by these environments.

We introduce push-grasping, a physics-based action to grasp an object first by pushing it and then closing the fingers. We analyze the mechanics of push-grasping and demonstrate its effectiveness under clutter and object pose uncertainty. We integrate a planning system based on push-grasping to the geometric planners traditionally used in grasping. We then show that a similar approach can be used to perform manipulation with environmental contact in cluttered environments. We present a planner where the robot can simultaneously push multiple obstacles out of the way while grasping an object through clutter.

In the second part of this thesis we focus on planning a sequence of actions to manipulate clutter. We present a planning framework to rearrange clutter using prehensile and nonprehensile primitives. We show that our planner succeeds in environments where planners which only use prehensile primitives fail. We then explore the problem of manipulating clutter to search for a hidden object. We formulate the problem as minimizing the expected time to find the target, present two algorithms, and analyze their complexity and optimality.